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A new smoothing method for solving ε -support vector regression (ε-SVR), tolerating a small error...
A new smoothing method for solving ε -support vector regression (ε-SVR), tolerating a small error in
fitting a given data sets nonlinearly is proposed in this study. Which is a smooth unconstrained
optimization reformulation of the traditional linear programming associated with a ε-insensitive support
vector regression. We term this redeveloped problem as ε-smooth support vector regression (ε-SSVR).
The performance and predictive ability of ε-SSVR are investigated and compared with other methods
such as LIBSVM (ε-SVR) and P-SVM methods. In the present study, two Oxazolines and Oxazoles
molecular descriptor data sets were evaluated. We demonstrate the merits of our algorithm in a series of
experiments. Primary experimental results illustrate that our proposed approach improves the
regression performance and the learning efficiency. In both studied cases, the predictive ability of the ε-
SSVR model is comparable or superior to those obtained by LIBSVM and P-SVM. The results indicate
that ε-SSVR can be used as an alternative powerful modeling method for regression studies. The
experimental results show that the presented algorithm ε-SSVR, , plays better precisely and effectively
than LIBSVMand P-SVM in predicting antitubercular activity